{
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   "source": [
    "sklearn是一个流行的Python机器学习库，它提供了许多机器学习算法的实现。\n",
    "    线性回归是其中的一个常见算法，用于建立输入特征和连续目标变量之间的线性关系模型。\n",
    "    您可以使用sklearn中的LinearRegression类来实现线性回归模型。以下是一个简单的示例代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1422b9ca-a9a8-40dc-a934-068ca6454cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19c87a60-b696-4cd8-a156-c6f700418c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备数据\n",
    "X = np.array([[1], [2], [3], [4]])\n",
    "y = np.array([2, 4, 6, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76027850-cf45-4a70-8a0c-2fc0b62bb55b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "\n",
    "# 拟合模型\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5371dc2-e78d-4acd-8983-c2e475cb8fbe",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: joblib in c:\\users\\yfb-server\\appdata\\local\\miniconda3\\lib\\site-packages (1.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.2 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b5d7d41-3b58-48f1-852f-7ef7bfa0badb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c41d4f8-a489-4eac-b9fb-5c26cf61aead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['skearn_linear_model.pkl']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'skearn_linear_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab3c152c-b8fa-4b90-8f0f-b57440ed6398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.]\n"
     ]
    }
   ],
   "source": [
    "x_new = np.array([[5]])\n",
    "y_pred = model.predict(x_new)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "190c2b27-8246-4844-ad94-c1c33bf38880",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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